This paper addresses the challenge of obtaining accurate finite element model updating (FEMU) results for complex flexible aircraft structures without relying on prohibitively large numbers of direct finite element evaluations. An adaptive two-stage framework is proposed by integrating Bayesian regularisation neural networks (BR-ANN) with trust-region resampling. In the global stage, a coarse-grained surrogate model is constructed via Latin hypercube sampling (LHS) and Bayesian inference, enabling the non-dominated sorting genetic algorithm II (NSGA-II) to identify the Pareto-front region containing promising candidate solutions. Subsequently, a trust-region strategy performs high-fidelity resampling within the local neighbourhood of the knee-point solution, establishing a refined surrogate model that reduces local surrogate bias and improves parameter convergence. Validation using the GARTEUR SM-AG19 benchmark aircraft demonstrates that the proposed method effectively mitigates the smoothing effect inherent in conventional global surrogate models while maintaining computational efficiency. The average modal frequency error was reduced from 4.56% to 2.39%, and the method supports physically interpretable updating of material and equivalent beam parameters, providing a reliable baseline for subsequent aeroelastic predictions.

As drones and unmanned aerial vehicles (UAVs) are used in different scenarios, a variety of potential risks and safety challenges have arisen. One of the threats is that an increasing number of UAV encounter events are found near the airport in recent years, which pose significant dangers to manned aircraft and result in accidents. However, only a few studies examine the impacts of these events and propose effective countermeasures to enhance safety. To unveil the risks of UAV risk events (incidents or accidents) and examine the mechanism with risk factors, this study uses a tree-augmented naive Bayes network (TAN-BN). This method analyses the relationships among risk factors and UAV accidents/incidents to assess the efficacy of risk mitigation measures. Environmental, technological and human factors are simultaneously considered in constructing the Bayesian network. The analysis results reveal 12 specific risk factors that are significantly associated with UAV accidents/incidents in UAV operation scenarios, among which flight control system failure (the most critical factor), remote communication failure, other aircraft approaching, loss of electrical power, adverse weather, electromagnetic interference, operational errors, violations and risk factors leading to loss-of-control in flight are recognised as the most prominent factors. Based on these, five targeted risk mitigation measures are comprehensively implemented and evaluated. Moreover, a case study using UAV operation data near the Guanghan airport is introduced to justify the generalisability of the proposed TAN-BN model and the effectiveness of risk mitigation measures.

The operational weather radar network is highly suitable for large-scale bird monitoring and early warning systems. This study discusses, compares and contrasts current state-of-the-art technologies for large-scale bird surveillance – including manual observation, satellite tracking, dedicated avian radar and the operational weather radar network – evaluating their respective strengths, limitations and applicability for different monitoring scenarios. Key research questions focus on how to extract reliable bird movement information from weather radar data and how such data can support ecological studies and aviation safety. From the perspective of radar signal analysis, we examine the characteristics of bird echoes in terms of reflectivity, altitude distribution, flight speed and direction. We further review and compare methodologies for distinguishing bird echoes from weather and ground clutter, including clutter suppression, meteorological echo removal, feature extraction, machine learning classification and cross-validation with auxiliary data. Our synthesis demonstrates that weather radar can effectively identify and track bird movements over wide areas, providing valuable data on migration patterns and flight behaviours. These findings have practical significance for applications in avian ecology research and bird strike prevention in aviation.

Modern aviation supports an ever-broader range of civil and military missions, and the airframes designed for these missions must satisfy stringent safety and performance requirements. The takeoff and landing phases are the most accident-prone portions of a flight despite representing only a short interval of the total block time, which makes the accurate prediction of takeoff speed a safety-relevant problem. A previous machine learning study addressed the takeoff-speed prediction problem of the Boeing 737-300 with classical regressors using pressure altitude, outside air temperature, gross weight and flap angle as the predictors. In the present work, the same regression problem is revisited under the deep learning paradigm. Four neural architectures are trained on an identical pre-processing pipeline and train-validation partition, namely a multilayer perceptron, a one-dimensional convolutional network, a long short-term memory network and a wide-and-deep architecture incorporating multi-head self-attention. Among the four candidates, the long short-term memory network attains the lowest root mean square error and mean square error on the unseen test file and is subsequently subjected to Bayesian hyperparameter optimisation through the Keras Tuner library. The predicted and the measured takeoff speeds are reported side by side for the first time in the deep learning literature for this airframe, and the simulation results indicate that the developed networks constitute an effective alternative tool for takeoff-speed prediction.

This study presents a generalised theory that describes the thermodynamical processes during mixing of a moist aircraft exhaust with the ambient air and allows one to decide whether or not a contrail forms. Usage of alternative fuels like hydrogen or ammonia increases the moisture content in aircraft plumes compared to current kerosene combustion. Our analysis compares the thermodynamic plume evolution for the classical mixing line and a novel generalised formulation. Additionally, both formulations are used to evaluate the limiting ambient temperature, above which an aircraft does not produce a contrail. We find that the inaccuracies introduced by the classical mixing line cancel each other out, leading to negligible differences between both formulations. Furthermore, the impact of potential heat and water vapour recuperation systems on contrail formation behind fuel-cell-propelled aircraft is investigated. Reducing the exhaust’s thermal energy by technical means increases the contrail formation propensity. Especially, if fuels with high hydrogen content are used, plumes with reduced heat content could reach supersaturation values above $ 500{\%}$. This can trigger liquid water droplet formation directly from the gas phase, a process absent in conventional contrail scenarios, and may increase the number of formed ice crystals drastically. A concurrent increase in ice crystal numbers and contrail formation propensity would increase the contrail-cirrus climate impact. To mitigate this scenario, our analysis identifies requirements on the reduction of exhaust water vapour to suppress contrail formation by technical means and reduce the potential contrail climate impact by fuel-cell-propelled aircraft.

The maximum take-off weight (MTOW) affects many aspects of aircraft design, such as performance, stability and control. It is the first and most important design variable that affects numerous aircraft design decisions. Understanding how parametric changes to the major design variables would likely affect the MTOW in the early stages of the preliminary design phase is crucial. This research introduces a ground-breaking approach to precision weight estimation in light aircraft design. Employing a statistical approach, specifically multilinear regression alone and coupled with p-value analysis, the study focuses on predicting the MTOW using datasets from aircraft still ‘in production’ and/or ‘in service’. The aircraft are categorised by landing gear arrangement and number of engines. Eight crucial design parameters that influence aircraft weight are used to determine the empirical models. The developed models successfully predict the MTOW with an error of less than 5% and outperform existing weight estimation techniques, empowering designers to conduct parametric studies that include essential design parameters at the early stages of aircraft design. The proposed methodology represents a paradigm shift in the field, offering a reliable and practical means of achieving precision in light aircraft weight predictions.

The widespread use of unmanned aerial vehicles (UAVs) has introduced significant security challenges, including unauthorised intrusions and privacy breaches. Conventional identification methods, such as radar and visual surveillance, often struggle to accurately distinguish between different UAV types or individual devices, limiting their effectiveness in countering these threats. To address these concerns, an enhanced empirical mode decomposition method has been developed. This method employs the Welch algorithm to extract power spectral density (PSD) features, which are then used to form the radio frequency fingerprint (RFF). Finally, a statistical classifier is employed to recognise and classify UAVs. The results of experimental studies indicate that this technique yields an average precision of 97% in binary classification (drone vs. no drone) and 84.7% in quaternary classifications (identifying four drone models). A comprehensive performance evaluation, conducted using confusion matrices and receiver operating characteristic (ROC) curves, demonstrates that the classifier performs well across all categories, with low misclassification rates.

This study investigates the effects of continuous atmospheric turbulence on the longitudinal and lateral-directional flight dynamics of a tailless unmanned combat aerial vehicle (UCAV) with a lambda wing planform. The absence of vertical tail surfaces reduces inherent stability and introduces strong coupling between flight modes, making disturbance rejection a critical challenge. The UCAV dynamics were linearised using small perturbation theory and represented in state-space form, with turbulence disturbances modelled using the Von Karman spectrum under light, moderate and severe intensities. The dynamic flight modes were evaluated with reference to the MIL-F-8785C flying quality requirements. State feedback and state observer-based controllers were initially designed using pole placement techniques to evaluate the performance of linear systems and gain insight into their flying qualities. A model predictive controller (MPC) was subsequently developed and compared with these controllers to assess their turbulence rejection and attitude tracking capabilities. Simulation results demonstrate that MPC provides superior robustness, effectively rejecting turbulence across all intensities while ensuring accurate pitch and roll attitude tracking. The findings provide new insights into the dynamics and control of tailless UCAVs under realistic turbulence conditions, supporting the viability of such configurations for mission-oriented applications.

Blended-wing-body (BWB) aircraft with distributed propulsion offers significant potential for aerodynamic efficiency. However, this tightly integrated configuration inherently operates under boundary layer ingestion (BLI) conditions. The ingested thick fuselage boundary layer, coupled with the strong aerodynamic interactions between the S-shaped intake and wing, generates adverse pressure gradients at the inlet alongside low total pressure recovery coefficients and high outlet distortion. This study investigates integrated flow control under high-altitude flight conditions using a BWB aerofoil integrated with a semi-embedded S-shaped intake model, incorporating energy analysis. During cruise, strong adverse pressure gradients between the wing shock wave and intake cause distinct lip separation, resulting in a low total pressure recovery coefficient and high distortion. Distributed dual-row suction generates secondary flow generates distributed secondary flow near the separation point with equivalent energy consumption. This enhances fuselage boundary layer mixing, reduces near-wall adverse pressure gradients and improves the total pressure recovery coefficient. The integration of internal jet control synergistically augments the benefits of external wing suction via distinct spatial mechanisms: central jets replenish core flow kinetic energy to suppress separation and improve the total pressure recovery coefficient, while side jets enhance mixing between separation zones and mainstream flow, mitigating secondary swirl losses and substantially reducing distortion at the aerodynamic interface plane (AIP). Energy analysis confirms that a modest energy input (equivalent to 7.4% of the mainstream kinetic energy) yields a significant increase in intake thrust, while simultaneously reducing total pressure and swirl distortion.

Aviation has a significant contribution to climate change, which is poised to increase in the coming years due to increasing demand in air travel. The A321 APPU aircraft could offer a significant improvement as it offers a synergistic combination of two interesting technologies – a fuel-flexible hydrogen combustion system combined with boundary layer ingestion, by introducing a hydrogen-powered auxiliary power and propulsion unit (APPU). This turboshaft engine is located in the tail cone and powers a boundary layer ingestion propulsor, producing approximately 15% of the thrust. To improve the efficiency of the APPU, the feasibility of the steam ijection and recovery (SIR) cycle is evaluated. This semi-closed water cycle can reduce fuel consumption and NOx emissions. Both the baseline and the SIR APPU are modelled in pyCycle, an open-source gas turbine parametric analysis tool. The baseline APPU engine was found to have a thermal efficiency of 45% and a mass of around 500 kg. The SIR cycle can reduce fuel consumption by up to 7% and decrease NOx emissions by approximately 33%, with an increase in engine mass of approximately 15%.

Demand forecasting is a key component of planning in the aeronautical sector, where intermittent and irregular demand patterns pose significant challenges for decision-making processes. Forecast combinations have emerged as an effective strategy to improve predictive performance by leveraging the complementary strengths of diverse models. However, the quality of such combinations depends heavily on the generation schemes for combination weights. Existing learning approaches, particularly those based on meta-learning, have shown strong performance but can require substantial computational resources, limiting their practicality in large-scale or real-time environments. This study investigates LightGBM, a learning algorithm based on decision trees, as an efficient meta-learning tool for generating forecast combination weights in intermittent demand, using real data from the aeronautical sector. The current study proposes a forecasting combination framework in which LightGBM learns to assign weights based on time series features, allowing for flexible adaptation to diverse demand patterns. The method is compared to XGBoost, particle swarm optimisation and traditional combination approaches across multiple forecasting horizons. The experiments include a diverse pool of standard forecasting models with parameter variations grounded in the literature, ensuring a wider range of behaviours and method diversity. Results show that the LightGBM combination achieves forecasting accuracy comparable to XGBoost (slightly better in some cases), while consistently reducing training times by $96.11{\rm{\% }}$ on average compared to state-of-the-art approaches. These findings demonstrate that the proposed LightGBM provides a scalable, high-performance solution for forecast combinations, particularly suited to environments with intermittent demand patterns.

With the development of the low-altitude economy, challenges and opportunities are gradually emerging. In potential crash accidents, it is necessary to enhance the crashworthiness of low-altitude aircraft to ensure the safety of the occupants. This paper studies different types of honeycomb energy-absorbing structures located at the bottom of the fuselage. Two types of honeycombs, hexagonal honeycomb (HEX) and re-entrant honeycomb (REH), are selected to construct an integrated energy-absorbing structure to absorb the impact kinetic energy during a crash accident. The effects of cellular type, unit size and wall thickness on the deformation mode and specific energy absorption (SEA) were systematically investigated. The results indicate that REH exhibits more stable hierarchical folding and a higher SEA. Furthermore, the study identifies an optimal geometric configuration, characterised by a specific combination of cellular size and wall thickness, that achieves the most effective balance between lightweight requirements and energy-absorbing capabilities.

This paper presents an approach based on an intelligent fault-tolerant (FT) mechanism for enhancing a monopulse auto-tracking architecture through the integration of machine learning (ML) techniques. This approach is applied for the case of Oran ground station (GS) communicating in the S/X band frequency range to ensure system robustness during faulty events. The latter are due to the degradation or sudden failure of neuralgic radio frequency (RF) components, with an emphasis on the low-noise amplifier (LNA). To address these vulnerabilities, a probabilistic likelihood function is defined, and its statistical parameters are optimised using the grey wolf optimiser (GWO) in order to provide a means of automatic switchover to the ML tracking signal receiver (ML-TSR) control mode upon anomaly detection. Three ML algorithms, namely eXtreme Gradient Boosting (XGBoost), decision trees, and K-nearest neighbors (KNN), were implemented and evaluated using a database spanning eight years (2017–2024), including the operational parameters of the ALSAT-1B GS. The experimental results show that the KNN model achieves higher accuracy than the other approaches. A validation process was then conducted during an actual satellite pass encompassing a nominal phase followed by a GS LNA failure phase. During the nominal phase, a root mean square error (RMSE) of only 0.46° in azimuth and 0.64° in elevation was observed. Activation of our recovery mechanism during the second phase demonstrated that the robustness of the likelihood function, combined with the accuracy of the ML-TSR controller, ensures mission continuity without data loss, leading to four successful image downloads.

Sled helicopters, equipped with sleds on landing gear, can land on thick snow-covered pavement. However, existing research focuses on hard-surface landings, with little investigation into snow-covered pavement, hampering safety assessments. This study analyses sled helicopter landing characteristics on snow-covered pavement. Using the capped Drucker–Prager snow constitutive model and the coupled Eulerian–Lagrangian method, a multi-field finite-element model of the helicopter-snow system is established. Symmetrical and asymmetrical landing characteristics are compared, and the effects of initial landing velocity on landing and sliding safety boundaries are simulated. Results show that under identical symmetrical landing parameters, the maximum vertical overload at the centre of mass is 1.11 g, 25% lower than for wheeled helicopters. After an asymmetrical landing, the helicopter remains tilted, increasing load on a single landing gear. At a 5° roll angle, the peak load on the right landing gear is 22.88% higher than at 0°. Without rotor pitch moment, a heading velocity exceeding 3.8 m/s risks overturning during landing. Based on these findings, the velocity-moment safety boundary for the safe landing of sled helicopters is presented.

As a revolutionary aviation propulsion system, the adaptive cycle engine (ACE) provides more powerful capabilities than conventional engines. To accurately exploit the ACE performance potential in a wide and complicated design space, the multi-objective optimisation towards enhancing the thrust and fuel efficiency based on the zero-dimensional and one-dimensional coupled (0D-1D) method is carried out. For the adaptive fan and variable geometry low-pressure turbine (LPT), the physics-based 1D mean-line models are established and coupled with the 0D engine model. Then, a multi-objective optimisation method is developed, which combines the back-propagation neural network (BPNN) and the non-dominated sorting genetic algorithm II (NSGA-II). At off-design conditions, the performance differences between the 0D-1D model and the 0D model are notable. In particular, when the 0D model is inaccurate in predicting the LPT characteristics with variable geometry, the ACE performance trends predicted by the 0D model may be opposite to reality. The Pareto-optimal thrust and fuel efficiency of the 0D-1D model are superior to those of the 0D model. Within the Pareto-optimal solutions, the subsonic specific fuel consumption (SFC) and thrust from the 0D-1D model can be 1.7% lower and 4.5% larger than those from the 0D model, respectively. The optimal design parameters and control laws between the two models are distinct.

Aircraft arresting systems (AAS) are critical safety components required for the operation of fighter aircraft, particularly during aborted take-off or emergency landing scenarios. The most significant challenge in the development and certification of these systems is the high-cost full-scale dead-load testing required to verify their performance under various aircraft mass and entry-speed conditions. This study presents a novel and fully domestic test system that reproduces the kinetic energy input defined in the MIL-STD-3036 standard by using high-inertia flywheels driven by electric servomotors. The proposed system stores the required energy in dual flywheels and transfers it to the arresting barrier through a controlled release mechanism, thereby replicating real aircraft-entry conditions without the need for jet engines, runways or physical dead-load vehicles. The study focuses on the conceptual design and analytical modelling of the proposed system. Dynamic analyses were conducted to determine flywheel geometry, material selection, allowable stresses, rotational speeds and energy absorption capacity. The system was shown to meet the highest energy level defined in the MIL-STD-3036 test matrix, while being designed to enable accurate and controlled evaluation of arresting force, torque response, angular deceleration and stopping time. Experimental validation has not yet been conducted and is planned as part of future work. The results demonstrate that the proposed test system provides an economical, repeatable and safe alternative to conventional dead-load tests and enables performance evaluation, maintenance verification and certification of AAS. Furthermore, the system offers a scalable platform for future arresting technologies, including those designed for unmanned aircraft and next-generation military platforms.

The structural optimisation of aircraft wings remains a critical task in modern, lightweight aeronautical design, where computational efficiency must be balanced against modeling fidelity. To that end, this paper presents an automated framework for the parametric finite element modeling (FEM) and surrogate-based optimisation (SBO) of an aircraft wing using MSC Patran and Nastran. The geometry and structural layout of the wing are generated through Patran Command Language (PCL) scripting, enabling fully parametric control of ribs, spars, stringers and thickness distributions. The automated model is then linked to MSC Nastran for static, global linear buckling and dynamic aeroelastic (flutter) analyses. To reduce the computational cost associated with repeated commercial FEM-based evaluations, surrogate models are constructed and used to drive the structural optimisation process. The framework is demonstrated on a representative high aspect-ratio wing structure, showing significant reductions in design cycle time while maintaining accuracy in predicting mass and performance metrics. The results highlight the potential of integrating surrogate modeling with commercial FEM software in an automated workflow, offering a practical and scalable approach for aerospace structural design and optimisation.

Vertical take-off and landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of urban air mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refuelling or charging and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimise vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilise multiple surface directions for climbing and approach. This flexibility necessitates specialised scheduling methods. We propose a mixed integer linear programme (MILP) formulation to holistically optimise vertiminal operations, including taxiing, climbing (or approach) using multiple directions and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the touchdown and lift-off (TLOF) pad system, taxiway system and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. As a case study, we applied our throughput analysis on a vertiminal topology in the literature and used our MILP to find the optimal configuration. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.

In this study, a hybrid propulsion-powered small fixed-wing unmanned aerial vehicle (UAV) was designed to enhance endurance using solar energy. The UAV, a solar-powered vertical take-off and landing (VTOL) with a 1.8 m wingspan and a take-off mass of 3.3 kg, was equipped with a propulsion system comprising solar cells, a battery, a supercapacitor and a DC/DC converter, which was modelled in MATLAB/Simulink to evaluate energy management strategies. To optimise energy utilisation, fuzzy logic (FL), equivalent consumption minimisation strategy (ECMS) and quantum particle swarm optimisation (QPSO) algorithms were implemented. Notably, the QPSO algorithm was integrated into the solar energy management system for the first time. Optimisation results indicate that the QPSO algorithm harnesses solar energy more rapidly and efficiently than other strategies, significantly improving the UAV’s endurance. The time required for the QPSO algorithm to reach maximum power is 1.7948 s and 1.5028 s, shorter than that of the FL and ECMS algorithms, respectively. This result demonstrates that the QPSO algorithm exhibits a fast dynamic response and adapts more efficiently to sudden power demands. Furthermore, considering the time required to reach the maximum power output of 77 W from the solar cell, the corresponding contributions to endurance are calculated as 1.22 h for QPSO, 0.51 h for FL and 0.06 h for ECMS.